




'''simple docstring'''


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase_					=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Any					,       a_						:				Optional[int]					,       a_						:				Optional[int]					,       a_						:				Optional[Any]      ):
  for attribute in key.split('.'      ):
    __a								=       getattr(a_					,       a_      )

  if weight_type is not None:
    __a								=       getattr(a_					,       a_      ).shape
  else:
    __a								=       hf_pointer.shape

  assert hf_shape == value.shape, (
      f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
      f" {value.shape} for {full_name}"
  )

  if weight_type == "weight":
    __a								=       value
  elif weight_type == "weight_g":
    __a								=       value
  elif weight_type == "weight_v":
    __a								=       value
  elif weight_type == "bias":
    __a								=       value
  else:
    __a								=       value

  logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Tuple      ):
  __a								=       []
  __a								=       fairseq_model.state_dict()

  __a								=       hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __a								=       False
    if "conv_layers" in name:
      load_conv_layer(
          a_					,       a_					,       a_					,       a_					,       hf_model.config.feat_extract_norm == 'group'					,       )
      __a								=       True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
          __a								=       True
          if "*" in mapped_key:
            __a								=       name.split(a_      )[0].split('.'      )[-2]
            __a								=       mapped_key.replace('*'					,       a_      )
          if "weight_g" in name:
            __a								=       'weight_g'
          elif "weight_v" in name:
            __a								=       'weight_v'
          elif "bias" in name and "relative_attention_bias" not in name:
            __a								=       'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __a								=       'weight'
          else:
            __a								=       None

          set_recursively(a_					,       a_					,       a_					,       a_					,       a_      )
        continue
    if not is_used:
      unused_weights.append(a_      )

  logger.warning(f"Unused weights: {unused_weights}"      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				Tuple					,       a_						:				Optional[Any]					,       a_						:				Optional[int]					,       a_						:				Optional[int]      ):
  __a								=       full_name.split('conv_layers.'      )[-1]
  __a								=       name.split('.'      )
  __a								=       int(items[0]      )
  __a								=       int(items[1]      )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f"{full_name} has size {value.shape}, but"
          f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
      )
      __a								=       value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f"{full_name} has size {value.shape}, but"
          f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
      )
      __a								=       value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
          " found."
      )
      __a								=       value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f"{full_name} has size {value.shape}, but"
          f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
      )
      __a								=       value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
  else:
    unused_weights.append(a_      )


@torch.no_grad()
def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]					,       a_						:				Dict					,       a_						:				Any=None      ):
  # load the pre-trained checkpoints
  __a								=       torch.load(a_      )
  __a								=       WavLMConfigOrig(checkpoint['cfg']      )
  __a								=       WavLMOrig(a_      )
  model.load_state_dict(checkpoint['model']      )
  model.eval()

  if config_path is not None:
    __a								=       WavLMConfig.from_pretrained(a_      )
  else:
    __a								=       WavLMConfig()

  __a								=       WavLMModel(a_      )

  recursively_load_weights(a_					,       a_      )

  hf_wavlm.save_pretrained(a_      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  UpperCAmelCase_					=   parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



